view _nnet_ops.py @ 259:621faba17c60

created 'dummytests', tests that checks consistency of new weird datasets, where we can't compare with actual values in a matrix, for instance. Useful as a first debugging when creating a dataset
author Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
date Tue, 03 Jun 2008 16:41:55 -0400
parents 3ef569b92fba
children
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import unittest
import theano._test_tensor as TT
import numpy

from nnet_ops import *

class T_sigmoid(unittest.TestCase):
    def setUp(self):
        numpy.random.seed(9999)
    def test_elemwise(self):
        TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)])

class T_softplus(unittest.TestCase):
    def setUp(self):
        numpy.random.seed(9999)
    def test_elemwise(self):
        TT.verify_grad(self, softplus, [numpy.random.rand(3,4)])

class T_CrossentropySoftmax1Hot(unittest.TestCase):
    def setUp(self):
        numpy.random.seed(9999)
    def test0(self):
        y_idx = [0,1,3]
        class Dummy(object):
            def make_node(self, a,b):
                return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1]
        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
            numpy.random.rand(4)])

    def test1(self):
        y_idx = [0,1,3]
        class Dummy(object):
            def make_node(self, a):
                return crossentropy_softmax_1hot(a, y_idx)[0:1]
        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])



if __name__ == '__main__':
    unittest.main()